FLoSS: Facility Location for Subspace Segmentation

Subspace segmentation is the task of segmenting data lying on multiple linear subspaces. Its applications in computer vision include motion segmentation in video, structure-from-motion, and image clustering. In this work, we describe a novel approach for subspace segmentation that uses probabilistic inference via a message-passing algorithm. We cast the subspace segmentation problem as that of choosing the best subset of linear subspaces from a set of candidate subspaces constructed from the data. Under this formulation, subspace segmentation reduces to facility location, a well studied operational research problem. Approximate solutions to this NP-hard optimization problem can be found by performing maximum-a-posteriori (MAP) inference in a probabilistic graphical model. We describe the graphical model and a message-passing inference algorithm. We demonstrate the performance of Facility Location for Subspace Segmentation, or FLoSS, on synthetic data as well as on 3D m...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Nevena Lazic, Inmar Givoni, Brendan Frey
• Keywords: Computer Vision, FLoSS, ICCV 2009, Subspace Segmentation